The proposed research seeks to advance the development of a global slum inventory. The first objective is to develop a new algorithm for mapping slums. To achieve this, a georeferenced dataset of slum communities, made available by the Brazilian government, will be used. The characteristics of this dataset make it well- suited for this research. Firstly, the slum areas are already georeferenced and labelled, a process that is often very time-consuming in machine learning projects. Secondly, it has data for many cities, which allows for models to be trained in different urban contexts. This has been demonstrated as a limitation of current studies and that would, consequently, contribute to the advancement of the state-of-the-art on the topic. The second objective is utilise machine learning techniques to quantify the population living in these settlements. To assess the suitability of the algorithms that will be developed in this research, the results will be compared with official census data. The models will also be evaluated in terms of the cost of acquiring the images, computation complexity and generalizability of the approach to other regions.